Bayram, EdaThanou, DorinaVural, ElifFrossard, Pascal2020-07-102020-07-102020-07-102020-01-0110.1109/TSIPN.2020.2995515https://infoscience.epfl.ch/handle/20.500.14299/169966WOS:0005429731000021910.10114Structure inference is an important task for network data processing and analysis in data science. In recent years, quite a few approaches have been developed to learn the graph structure underlying a set of observations captured in a data space. Although real-world data is often acquired in settings where relationships are influenced by a priori known rules, such domain knowledge is still not well exploited in structure inference problems. In this paper, we identify the structure of signals defined in a data space whose inner relationships are encoded by multi-layer graphs. We aim at properly exploiting the information originating from each layer to infer the global structure underlying the signals. We thus present a novel method for combining the multiple graphs into a global graph using mask matrices, which are estimated through an optimization problem that accommodates the multi-layer graph information and a signal representation model. The proposed mask combination method also estimates the contribution of each graph layer in the structure of signals. The experiments conducted both on synthetic and real-world data suggest that integrating the multi-layer graph representation of the data in the structure inference framework enhances the learning procedure considerably by adapting to the quality and the quantity of the input data.Engineering, Electrical & ElectronicTelecommunicationsEngineeringTelecommunicationstask analysissocial network servicessemisupervised learningsignal representationdata analysislaplace equationsinformation processingmulti-relational networksmulti-view data analysisnetwork data analysisgraph signal processingstructure inferencelink predictiongraph learningprecision matrixnetworktext::journal::journal article::research article